{"list":"other","total":52,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1506.01497","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","created":"2015-06-04","cited_by_count":9639},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"1612.03144","title":"Feature Pyramid Networks for Object Detection","created":"2016-12-09","cited_by_count":5036},{"id":"1709.01507","title":"Squeeze-and-Excitation Networks","created":"2017-09-05","cited_by_count":4789},{"id":"1512.02325","title":"SSD: Single Shot MultiBox Detector","created":"2015-12-08","cited_by_count":3130},{"id":"1504.08083","title":"Fast R-CNN","created":"2015-04-30","cited_by_count":2933},{"id":"1611.05431","title":"Aggregated Residual Transformations for Deep Neural Networks","created":"2016-11-16","cited_by_count":2546},{"id":"1711.07971","title":"Non-local Neural Networks","created":"2017-11-21","cited_by_count":2293},{"id":"1904.01355","title":"FCOS: Fully Convolutional One-Stage Object Detection","created":"2019-04-02","cited_by_count":1577},{"id":"1612.08242","title":"YOLO9000: Better, Faster, Stronger","created":"2016-12-25","cited_by_count":1431},{"id":"1506.02025","title":"Spatial Transformer Networks","created":"2015-06-05","cited_by_count":1429},{"id":"1502.03044","title":"Show, Attend and Tell: Neural Image Caption Generation with Visual Attention","created":"2015-02-10","cited_by_count":1423},{"id":"1902.09630","title":"Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression","created":"2019-02-25","cited_by_count":1368},{"id":"1712.00726","title":"Cascade R-CNN: Delving into High Quality Object Detection","created":"2017-12-03","cited_by_count":1276},{"id":"1904.07850","title":"Objects as Points","created":"2019-04-16","cited_by_count":1175},{"id":"1708.02002","title":"Focal Loss for Dense Object Detection","created":"2017-08-07","cited_by_count":752},{"id":"1912.02424","title":"Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection","created":"2019-12-05","cited_by_count":540},{"id":"1911.08287","title":"Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression","created":"2019-11-19","cited_by_count":459},{"id":"1605.06409","title":"R-FCN: Object Detection via Region-based Fully Convolutional Networks","created":"2016-05-20","cited_by_count":414},{"id":"1907.12209","title":"Enforcing geometric constraints of virtual normal for depth prediction","created":"2019-07-29","cited_by_count":234},{"id":"1702.05693","title":"CityPersons: A Diverse Dataset for Pedestrian Detection","created":"2017-02-19","cited_by_count":172},{"id":"2007.08103","title":"Probabilistic Anchor Assignment with IoU Prediction for Object Detection","created":"2020-07-16","cited_by_count":150},{"id":"1711.06897","title":"Single-Shot Refinement Neural Network for Object Detection","created":"2017-11-18","cited_by_count":129},{"id":"1909.02466","title":"FreeAnchor: Learning to Match Anchors for Visual Object Detection","created":"2019-09-05","cited_by_count":90},{"id":"1901.03278","title":"Region Proposal by Guided Anchoring","created":"2019-01-10","cited_by_count":87},{"id":"1607.07155","title":"A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection","created":"2016-07-25","cited_by_count":83},{"id":"1612.04402","title":"Finding Tiny Faces","created":"2016-12-13","cited_by_count":72},{"id":"1711.07752","title":"Repulsion Loss: Detecting Pedestrians in a Crowd","created":"2017-11-21","cited_by_count":62},{"id":"2003.09163","title":"Detection in Crowded Scenes: One Proposal, Multiple Predictions","created":"2020-03-20","cited_by_count":59},{"id":"1807.08407","title":"Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd","created":"2018-07-23","cited_by_count":58},{"id":"1803.09937","title":"Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification","created":"2018-03-27","cited_by_count":50},{"id":"1607.07032","title":"Is Faster R-CNN Doing Well for Pedestrian Detection?","created":"2016-07-24","cited_by_count":37},{"id":"1910.06160","title":"Mask-Guided Attention Network for Occluded Pedestrian Detection","created":"2019-10-14","cited_by_count":36},{"id":"2003.12729","title":"NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing","created":"2020-03-28","cited_by_count":36},{"id":"1510.08160","title":"Scale-aware Fast R-CNN for Pedestrian Detection","created":"2015-10-28","cited_by_count":32},{"id":"1703.01425","title":"Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection","created":"2017-03-04","cited_by_count":31},{"id":"1909.06826","title":"PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes","created":"2019-09-15","cited_by_count":30},{"id":"1602.01237","title":"How Far are We from Solving Pedestrian Detection?","created":"2016-02-03","cited_by_count":29},{"id":"1706.08564","title":"Illuminating Pedestrians via Simultaneous Detection & Segmentation","created":"2017-06-26","cited_by_count":26},{"id":"1807.00980","title":"MetaAnchor: Learning to Detect Objects with Customized Anchors","created":"2018-07-03","cited_by_count":21},{"id":"1507.05348","title":"Learning Complexity-Aware Cascades for Deep Pedestrian Detection","created":"2015-07-19","cited_by_count":18},{"id":"1501.05790","title":"Taking a Deeper Look at Pedestrians","created":"2015-01-23","cited_by_count":15},{"id":"1909.10674","title":"Relational Learning for Joint Head and Human Detection","created":"2019-09-24","cited_by_count":14},{"id":"1812.00440","title":"Pedestrian Detection with Autoregressive Network Phases","created":"2018-12-02","cited_by_count":12},{"id":"1610.03466","title":"Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection","created":"2016-10-11","cited_by_count":10},{"id":"1801.01849","title":"Hi-Fi: Hierarchical Feature Integration for Skeleton Detection","created":"2018-01-05","cited_by_count":6},{"id":"1701.01692","title":"To Boost or Not to Boost? On the Limits of Boosted Trees for Object Detection","created":"2017-01-06","cited_by_count":4},{"id":"1706.06768","title":"Saliency Guided End-to-End Learning for Weakly Supervised Object Detection","created":"2017-06-21","cited_by_count":3},{"id":"1807.01438","title":"Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation","created":"2018-07-04","cited_by_count":2}],"next_cursor":null}